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SUMMARY: 5′‐3′ exoribonucleases (XRNs) play crucial roles in the control of RNA processing, quality, and quantity in eukaryotes. Although genome‐wide profiling of RNA decay fragments is now feasible, how XRNs shape the plant mRNA degradome remains elusive. Here, we profiled and analyzed the RNA degradomes of Arabidopsis wild‐type and mutant plants with defects in XRN activity. Deficiency of nuclear XRN3 or cytoplasmic XRN4 activity but not nuclear XRN2 activity greatly altered Arabidopsis mRNA decay profiles. Short excised linear introns and cleaved pre‐mRNA fragments downstream of polyadenylation sites were polyadenylated and stabilized in the xrn3 mutant, demonstrating the unique function of XRN3 in the removal of cleavage remnants from pre‐mRNA processing. Further analysis of stabilized XRN3 substrates confirmed that pre‐mRNA 3′ end cleavage frequently occurs after adenosine. The most abundant decay intermediates in wild‐type plants include not only the primary substrates of XRN4 but also the products of XRN4‐mediated cytoplasmic decay. An increase in decay intermediates with 5′ ends upstream of a consensus motif in the xrn4 mutant suggests that there is an endonucleolytic cleavage mechanism targeting the 3′ untranslated regions of many Arabidopsis mRNAs. However, analysis of decay fragments in the xrn4 mutant indicated that, except for microRNA‐directed slicing, endonucleolytic cleavage events in the coding sequence rarely result in major decay intermediates. Together, these findings reveal the major substrates and products of nuclear and cytoplasmic XRNs along Arabidopsis transcripts and provide a basis for precise interpretation of RNA degradome data. [ABSTRACT FROM AUTHOR]

Objective: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods., Method: The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE)., Result: For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model's robustness., Conclusion: GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Hou, Xie, Feng and Dong.)